Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes

Association between continuous glucose monitoring metrics and clinical outcomes in adults with type 1 diabetes in a real-world setting

AIMS

Continuous glucose monitoring (CGM) metrics can assist diabetes management. Consensus statements recommend > 70 % time in range (TIR) and ≤ 36 % glucose coefficient of variation (CV). However, how these targets perform in clinical practice is unknown. This retrospective, longitudinal cohort study analyzed relationships between TIR, CV, glycated hemoglobin (HbA1c), and hypoglycemia in a real-world setting.

METHODS

Data of 542 adults with type 1 diabetes who used CGM (January 2014-July 2020) were analyzed. Associations between TIR and HbA1c at the same and subsequent visits, incidence rate ratios (IRRs) for hypoglycemia at different CVs, and number of hypoglycemic events at cross-sections of HbA1c and CV were estimated by regression.

RESULTS

TIR was inversely related to HbA1c; for every 10 % increase in TIR, HbA1c was significantly reduced by 0.34 % (4 mmol/mol) and 0.20 % (2 mmol/mol) at the same and subsequent visits, respectively. Level 2 hypoglycemia was significantly reduced at CV < 30 %, 30-33 %, 33.1-36 %, and 36.1-40 %: adjusted IRRs vs CV ≥ 40.1 % of 0.14, 0.28, 0.32, and 0.50, respectively. Hypoglycemic events were reduced at lower CV across HbA1c levels and at higher HbA1c across CV levels.

CONCLUSION

This study quantifies HbA1c improvements with increased TIR and hypoglycemia reductions with improved CV in clinical practice.

Correlation Between Blood Urea Nitrogen and Short- and Long-Term Glycemic Variability in Elderly Patients with Type 2 Diabetes Mellitus Who Were hospitalized:A Retrospective Study

Objective

Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and progressively impaired insulin secretion resulting in dynamic fluctuations in glucose levels.High blood urea nitrogen (BUN) levels have been linked to decreased insulin sensitivity, suppressed insulin synthesis and increased risk of incident diabetes mellitus in humans as well as insulin use in patients with T2DM.This study characterize the association between BUN levels and short-term and long-term glycemic variability(GV) in the elderly patients with T2DM who were hospitalized.

Methods

A total of 927 elderly patients with T2DM were included in the study. The short-term GV was quantified using parameters such as standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), based on multi-point fingertip blood glucose monitoring. The long-term GV was quantified using parameters such as SD, CV, variation independent of the mean (VIM), and average successive variability (ARV), based on fasting blood glucose(FPG). The relationship between BUN levels and short-term and long-term GV in elderly T2DM who were hospitalized was explored using methods such as Spearman correlation coefficient, linear regression analysis, logistic regression analysis, and interaction tests.

Results

In elderly patients with T2DM were hospitalized, there is a significant correlation between BUN levels and both short-term and long-term GV. BUN is negatively correlated with the GV parameter TIR (r=-0.12, P=0.000), and positively correlated with SD (r=0.12, P=0.000), CV (r=0.07, P=0.026), MAGE (r=0.11, P=0.001), FPG-SD (r=0.08, P=0.013), and FPG-CV (r=0.08, P=0.014).Furthermore, the association remains consistent across different age, gender, BMI, and haemoglobin A1c (HbA1c) subgroups (P interaction > 0.05).

Conclusion

In elderly patients with T2DM were hospitalized, BUN levels were positively associated with GV.Therefore, monitoring BUN levels were beneficial in assessing the degree of GV.

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study

AIMS

Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV.

METHODS

We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models.

RESULTS

A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV.

CONCLUSIONS

An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes

AIMS

To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic β-cell function of subjects with type 2 diabetes mellitus (T2DM).

METHODS

A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic β-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables.

RESULTS

Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of β-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects.

CONCLUSION

Our study suggests that impaired pancreatic β-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

Association between brain imaging biomarkers and continuous glucose monitoring-derived glycemic control indices in Japanese patients with type 2 diabetes mellitus

INTRODUCTION

Although type 2 diabetes mellitus (T2DM) is associated with alterations in brain structure, the relationship between glycemic control indices and brain imaging markers remains unclear. This study aimed to investigate the association between continuous glucose monitoring (CGM)-derived glycemic control indices and brain imaging biomarkers assessed by MRI.

RESEARCH DESIGN AND METHODS

This cross-sectional study included 150 patients with T2DM. The severity of cerebral white matter lesions (WMLs) was assessed using MRI for deep and subcortical white matter and periventricular hyperintensities. The degree of medial temporal lobe atrophy (MTA) was assessed using voxel-based morphometry. Each participant wore a retrospective CGM for 14 consecutive days, and glycemic control indices, such as time in range (TIR) and glycemia risk index (GRI), were calculated.

RESULTS

The proportion of patients with severe WMLs showed a decreasing trend with increasing TIR (P for trend=0.006). The proportion of patients with severe WMLs showed an increasing trend with worsening GRI (P for trend=0.011). In contrast, no significant association was observed between the degree of MTA and CGM-derived glycemic control indices, including TIR (P for trend=0.325) and GRI (P for trend=0.447).

CONCLUSIONS

The findings of this study indicate that the severity of WMLs is associated with TIR and GRI, which are indices of the quality of glycemic control.

TRIAL REGISTRATION NUMBER

UMIN000032143.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Interaction between haptoglobin genotype and glycemic variability on diabetic macroangiopathy: a population-based cross-sectional study

PURPOSE

Haptoglobin (Hp) is a hemoglobin-binding protein that functions as an antioxidant in human plasma. It is reported that glycemic variability (GV) plays a key role in diabetes-related complications associated with impaired glucose metabolism and oxidative stress. Here we aim to investigate whether the effect of GV on diabetic macroangiopathy depends on Hp genotype in type 2 diabetes.

METHODS

A number of 860 Chinese patients with type 2 diabetes was genotyped and assigned to two Hp subgroups (Hp 2-2 and Hp 1 carriers). Glycemic variability (GV) was assessed by using a retrospective continuous glucose monitoring system for three consecutive days, and it was measured using the glucose coefficient of variation (%CV), which is calculated as the ratio of glucose standard deviation to glucose mean. Clinical features, history of cardiac surgery, and vascular imaging tests were utilized to diagnose macroangiopathy. We evaluated the interaction between Hp genotypes and %CV on diabetic macroangiopathy. Furthermore, serum concentration of 8-hydroxy-2'-deoxyguanosine (8-OHdG) was measured using an enzyme-linked immunosorbent assay as a biomarker of oxidative stress.

RESULTS

Serum 8-OHdG levels were positively correlated with %CV in Hp 1 carriers (r = 0.117; p = 0.021). Patients in the highest %CV tertile were associated with a higher prevalence of diabetic macroangiopathy than those in the lowest %CV tertile in Hp 1 carriers (OR = 2.461 [95% CI, 1.183-5.121], p = 0.016), but not in those with Hp 2-2 genotype (OR = 0.540 [95% CI, 0.245-1.191], p = 0.127). A significant interactive effect of Hp genotypes and %CV on diabetic macroangiopathy was found (p interaction = 0.008).

CONCLUSION

Hp genotype modifies the effect of GV on diabetic macroangiopathy among Chinese patients with type 2 diabetes.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Cardiovascular autonomic neuropathy is associated with increased glycemic variability driven by hyperglycemia rather than hypoglycemia in patients with diabetes

AIM

Cardiac autonomic neuropathy (CAN) has been suggested to be associated with hypoglycemia and impaired hypoglycemia unawareness. We have assessed the relationship between CAN and extensive measures of glucose variability (GV) in patients with type 1 and type 2 diabetes.

METHODS

Participants with diabetes underwent continuous glucose monitoring (CGM) to obtain measures of GV and the extent of hyperglycemia and hypoglycemia and cardiovascular autonomic reflex testing.

RESULTS

Of the 40 participants (20 T1DM and 20 T2DM) (aged 40.70±13.73 years, diabetes duration 14.43±7.35 years, HbA1c 8.85±1.70%), 23 (57.5%) had CAN. Despite a lower coefficient of variation (CV) (31.26±11.87 vs. 40.33±11.03, P=0.018), they had a higher CONGA (8.42±2.58 vs. 6.68±1.88, P=0.024) with a lower median LBGI (1.60 (range: 0.20-3.50) vs. 4.90 (range: 3.20-7.40), P=0.010) and percentage median time spent in hypoglycemia (4 (range:4-13) vs. 1 (range:0-5), P=0.008), compared to those without CAN. The percentage GRADE_Euglycemia (3.30±2.78 vs. 5.69±3.09, P=0.017) and GRADE_Hypoglycemia (0.3 (range: 0 - 3.80) vs. 1.8 (range: 0.9-6.5), P=0.036) were significantly lower, while the percentage median GRADE_{Hyperglycemia} (95.45 (range:93-98) vs. 91.6 (82.8-95.1), P=0.013) was significantly higher in participants with CAN compared to those without CAN.

CONCLUSION

CAN was associated with increased glycemic variability with less time in euglycemia attributed to a greater time in hyperglycemia but not hypoglycemia.

Alternative Design of One-Sided Shewhart Control Charts for the Multivariate Coefficient of Variation

The control charting technique is an approach to quality control and was implemented in various industries. There are many control charts, where the coefficient of variation control chart was one of the common charts and greatly used in Statistical Process Control. Since most processes are multivariate, the multivariate coefficient of variation charts has received great attention in the past few years. However, the existing multivariate coefficient of variation control charts was evaluated in terms of the average run length criterion, which may misinterpret the actual performance of the charts. This paper designs an alternative for the Shewhart multivariate coefficient of variation chart by considering the median run length and expected median run-length criteria to circumvent this problem. The research on the multivariate coefficient of variation chart is very limited in the existing literature by considering the median run length criterion. This proposed chart in this paper can minimize this research gap. The formulas and algorithms of the proposed chart are presented. The outputs of the proposed charts are shown by examining the different upward and downward process shifts. Additionally, the sample sizes, the process shifts, and the variation of the run-length distribution are investigated for their effects on the proposed chart. The findings reveal that the run-length distribution’s variation is inversely proportional to the shift size. Furthermore, it shows that the variation decreases if the shift size increases.

Clinical application guidelines for blood glucose monitoring in China (2022 edition)

Glucose monitoring is an important component of diabetes management. The Chinese Diabetes Society (CDS) has been producing evidence-based guidelines on the optimal use of glucose monitoring since 2011. In recent years, new technologies in glucose monitoring and more clinical evidence, especially those derived from Chinese populations, have emerged. In this context, the CDS organised experts to revise the Clinical application guidelines for blood glucose monitoring in China in 2021. In this guideline, we focus on four methods of glucose monitoring that are commonly used in clinical practice, including capillary glucose monitoring, glycated haemoglobin A1c, glycated albumin, and continuous glucose monitoring. We describe the definitions and technical characteristics of these methods, the factor that may interfere with the measurement, the advantages and caveats in clinical practice, the interpretation of glucose metrics, and the relevant supporting evidence. The recommendations for the use of these methods are also provided. The various methods of glucose monitoring have their strengths and limitations and cannot be replaced by one another. We hope that these guidelines could aid in the optimal application of common methods of glucose monitoring in clinical practice for better diabetes care.

Medical Certification of Pilots Through the Insulin-Treated Diabetes Mellitus Protocol at the FAA

INTRODUCTION: In 2019, the Federal Aviation Administration (FAA) announced a protocol to evaluate pilots with insulin treated diabetes mellitus (ITDM) for special issuance (SI) medical certification for first-/second-class pilots. The protocol’s aim is improved assessment of ITDM control/hypoglycemia risk and relies on continuous glucose monitoring (CGM) data. This study compares the characteristics of first-/second-class pilots with ITDM and certification outcome.METHODS: Data was collected retrospectively from the FAA Document Imaging Workflow System (DIWS) for pilots considered for a first-/second-class SI under the ITDM program between November 2019 and October 2021. Inclusion criteria required submission of information required for certification decision (SI vs. denial). We extracted data on demographics and CGM parameters including mean glucose, standard deviation, coefficient of variance, time in range (%), time > 250 mg · dl−1 (%), and time < 70–80 mg · dl−1 (%). We compared these parameters between pilots issued an SI vs. denial with Mann-Whitney U-tests and Fisher exact tests using R.RESULTS: Of 200 pilots with ITDM identified, 77 met inclusion criteria. Of those, 55 received SIs and 22 were denied. Pilots issued SI were statistically significantly older (46 vs. 27 yr), had a lower hemoglobin A1c (6.50% vs. 7.10%), lower average glucose (139 mg · dl−1 vs. 156 mg · dl−1), and spent less time with low glucose levels (0.95% vs. 2.0%).DISCUSSION: The FAA program has successfully medically certificated pilots with ITDM for first-/second-class. Pilots granted an ITDM SI reflect significantly better diabetes control, including less potential for hypoglycemia. As this program continues, it will potentially allow previously disqualified pilots to fly safely.Stanwyck LK, DeVoll JR, Pastore J, Gamble Z, Poe A, Gui GV. Medical certification of pilots through the insulin-treated diabetes mellitus protocol at the FAA. Aerosp Med Hum Perform. 2022; 93(8):627–632.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.